Multiple-input-multiple-output (MIMO) communication systems can have significantly higher channel capacity than single-input-single-output (SISO) systems for the same total transmission power and bandwidth [1]. In wireless communications, MIMO systems have the ability to deal with multipath propagation [1] [2]. It is also known that capacity of MIMO systems increases with the number of antennas. However, in practical communication systems, combining signals carried by a larger number of antennas increases the number of RF chains, which increases the cost of the overall system. In [4], Molisch et al. showed that hardware cost can be significantly reduced by selecting a good subset of antennas from the set of physically available antennas and using the signals from the selected antennas only, without much sacrificing the advantage of multi antenna diversity. Which subset of antennas is good depends on the channels' conditions. Therefore, one can embody a MIMO communication system that has a larger number of antennas than the number of RF chains and selects, on the basis of the channels' conditions, a subset of antennas to which to connect the RF chains. Therefore, a need exists for antenna selection scheme that has low computational complexity and better performance. Especially, for wireless communications, the channels conditions can vary in time rapidly and the communication systems may have to change its selection of the antennas frequently in order to maintain high performance in communication. Therefore, computational efficiency of the antenna selection algorithm is important for adapting the antennas selection quickly to changing channel conditions.
No polynomial-time algorithm is known to select the antennas optimally. Finding an optimal selection of antennas can require a large amount of processing at the receiver side and thus result in a long processing delay and high processing power consumption. Due to the high computational complexity of the optimal selection, a number of suboptimal solutions with lower complexity were proposed in literature [5] [6] [7] [8] [9] [10]. There are a few patents on antenna selection method, e.g. for joint transmit/receive antenna selection [I] and receive antenna selection [II] [III] [IV]. The complexity an algorithm and the performance of a MIMO communication system depend on the number of transmit/receive antennas; i.e., complexity of an algorithm increases with the number of antennas and the performance of a MIMO communication system improves if the number of antennas increases.
A major aim for transmit or receive antenna selection schemes in the literature is to determine a good selection of antennas with low computational complexity. Different receive antenna selection algorithms are proposed in [5][6][8][9][10], and similarly a number of transmit antenna selection algorithms are proposed in [3][4][7]. All these proposed algorithms are presented to reduce the complexity of antenna selection while obtaining a good selection. There is tradeoff between the goodness of a selection and the computational amount to determine the selection. Our proposed transmit and/or receive antenna selection algorithm shows a better goodness-computation tradeoff.
Most of the antenna selection schemes are either proposed for receive antenna selection [4][5][6][7][8][9][10] or transmit antenna selection [1][2][3] separately. The main drawback of separate antenna selection is that hardware cost can only reduce at one side (either transmit side or receive side). In [13] authors proposed a kind of joint antenna selection scheme by performing separate exhaustive search on transmit and receive side. (This technique is termed as Decoupled antenna selection.) Decoupled antenna selection has two disadvantages 1) its complexity is high and 2) its performance is not close to the optimal performance. Our proposed joint antenna selection algorithm not only searches for a near optimum solution in real time but also has low computational complexity than all previous joint antenna selection algorithms.